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Extracting and Visualizing Stock Data

Description

Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.

Table of Contents

  • Define a Function that Makes a Graph
  • Question 1: Use yfinance to Extract Stock Data
  • Question 2: Use Webscraping to Extract Tesla Revenue Data
  • Question 3: Use yfinance to Extract Stock Data
  • Question 4: Use Webscraping to Extract GME Revenue Data
  • Question 5: Plot Tesla Stock Graph
  • Question 6: Plot GameStop Stock Graph

Estimated Time Needed: 30 min


*Note*:- If you are working in IBM Cloud Watson Studio, please replace the command for installing nbformat from !pip install nbformat==4.2.0 to simply !pip install nbformat

In [1]:
!pip install yfinance==0.1.67
!mamba install bs4==4.10.0 -y
!pip install nbformat==4.2.0
Collecting yfinance==0.1.67
  Downloading yfinance-0.1.67-py2.py3-none-any.whl (25 kB)
Requirement already satisfied: pandas>=0.24 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (1.3.5)
Requirement already satisfied: numpy>=1.15 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (1.21.6)
Requirement already satisfied: requests>=2.20 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (2.29.0)
Requirement already satisfied: multitasking>=0.0.7 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (0.0.11)
Requirement already satisfied: lxml>=4.5.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from yfinance==0.1.67) (4.9.2)
Requirement already satisfied: python-dateutil>=2.7.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=0.24->yfinance==0.1.67) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from pandas>=0.24->yfinance==0.1.67) (2023.3)
Requirement already satisfied: charset-normalizer<4,>=2 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.20->yfinance==0.1.67) (3.1.0)
Requirement already satisfied: idna<4,>=2.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.20->yfinance==0.1.67) (3.4)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.20->yfinance==0.1.67) (1.26.15)
Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from requests>=2.20->yfinance==0.1.67) (2023.5.7)
Requirement already satisfied: six>=1.5 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance==0.1.67) (1.16.0)
Installing collected packages: yfinance
  Attempting uninstall: yfinance
    Found existing installation: yfinance 0.2.4
    Uninstalling yfinance-0.2.4:
      Successfully uninstalled yfinance-0.2.4
Successfully installed yfinance-0.1.67

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        mamba (1.4.2) supported by @QuantStack

        GitHub:  https://github.com/mamba-org/mamba
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Looking for: ['bs4==4.10.0']

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pkgs/main/linux-64 ━━━━━━━╸━━━━━━━━━━━━━━━━━   0.0 B /  ??.?MB @  ??.?MB/s  0.0s[+] 0.1s
pkgs/main/linux-64 ━━━━━━━╸━━━━━━━━━━━━━━━━━   0.0 B /  ??.?MB @  ??.?MB/s  0.1s
pkgs/main/noarch   ━━━━━━━━╸━━━━━━━━━━━━━━━━   0.0 B /  ??.?MB @  ??.?MB/s  0.1s
pkgs/r/linux-64    ╸━━━━━━━━━━━━━━━╸━━━━━━━━   0.0 B /  ??.?MB @  ??.?MB/s  0.1s
pkgs/r/noarch      ━━━━━━━━━╸━━━━━━━━━━━━━━━   0.0 B /  ??.?MB @  ??.?MB/s  0.1s[+] 0.2s
pkgs/main/linux-64 ━━━━━━━━━╸━━━━━━━━━━━━━━━  16.4kB /  ??.?MB @ 107.2kB/s  0.2s
pkgs/main/noarch   ━━━━━━━━━━╸━━━━━━━━━━━━━━  86.0kB /  ??.?MB @ 562.0kB/s  0.2s
pkgs/r/linux-64    ━━━╸━━━━━━━━━━━━━━━╸━━━━━  57.4kB /  ??.?MB @ 374.0kB/s  0.2s
pkgs/r/noarch      ━━━━━━━━━━━╸━━━━━━━━━━━━━  73.7kB /  ??.?MB @ 479.8kB/s  0.2s[+] 0.3s
pkgs/main/linux-64 ━━━━━━━━━━━━╸━━━━━━━━━━━ 516.1kB @   2.0MB/s             0.3s
pkgs/main/noarch   ━━━━━━━━━━━━━━━━━━━━━━━━ 852.1kB @   2.9MB/s Finalizing  0.3s
pkgs/r/linux-64    ━━━━╸━━━━━━━━━━━━━━━╸━━━ 610.3kB @   2.4MB/s             0.3s
pkgs/r/noarch      ━━━━━━━━━━━━━╸━━━━━━━━━━ 622.6kB @   2.4MB/s             0.3spkgs/main/noarch                                   @   2.9MB/s  0.3s
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pkgs/main/linux-64 ━━━━━━━━━━━━━━╸━━━━━━━━━   1.1MB @   3.1MB/s             0.4s
pkgs/r/linux-64    ━━━━━━━╸━━━━━━━━━━━━━━━━   1.2MB @   3.3MB/s             0.4s
pkgs/r/noarch      ━━━━━━━━━━━━━━━━━━━━━━━━   1.3MB @   3.4MB/s Finalizing  0.4spkgs/r/noarch                                      @   3.4MB/s  0.4s
pkgs/r/linux-64                                    @   3.4MB/s  0.4s
[+] 0.5s
pkgs/main/linux-64 ━╸━━━━━━━━━━━━━━━╸━━━━━━━   1.8MB /  ??.?MB @   3.7MB/s  0.5s[+] 0.6s
pkgs/main/linux-64 ━━━╸━━━━━━━━━━━━━━━╸━━━━━   2.3MB /  ??.?MB @   4.0MB/s  0.6s[+] 0.7s
pkgs/main/linux-64 ━━━━━╸━━━━━━━━━━━━━━━╸━━━   2.8MB /  ??.?MB @   4.2MB/s  0.7s[+] 0.8s
pkgs/main/linux-64 ━━━━━━━━╸━━━━━━━━━━━━━━━━   3.3MB /  ??.?MB @   4.2MB/s  0.8s[+] 0.9s
pkgs/main/linux-64 ━━━━━━━━━━╸━━━━━━━━━━━━━━   3.9MB /  ??.?MB @   4.4MB/s  0.9s[+] 1.0s
pkgs/main/linux-64 ━━━━━━━━━━━━━╸━━━━━━━━━━━   4.4MB /  ??.?MB @   4.5MB/s  1.0s[+] 1.1s
pkgs/main/linux-64 ━━━━━━━╸━━━━━━━━━━━━━━━━━   5.0MB /  ??.?MB @   4.6MB/s  1.1s[+] 1.2s
pkgs/main/linux-64 ━━━━━━━━━╸━━━━━━━━━━━━━━━   5.6MB /  ??.?MB @   4.7MB/s  1.2s[+] 1.3s
pkgs/main/linux-64 ━━━━━━━━━━━╸━━━━━━━━━━━━━   5.9MB /  ??.?MB @   4.7MB/s  1.3spkgs/main/linux-64                                   6.0MB @   4.8MB/s  1.4s

Pinned packages:
  - python 3.7.*


Transaction

  Prefix: /home/jupyterlab/conda/envs/python

  Updating specs:

   - bs4==4.10.0
   - ca-certificates
   - certifi
   - openssl


  Package               Version  Build         Channel                 Size
─────────────────────────────────────────────────────────────────────────────
  Install:
─────────────────────────────────────────────────────────────────────────────

  + bs4                  4.10.0  hd3eb1b0_0    pkgs/main/noarch        10kB

  Upgrade:
─────────────────────────────────────────────────────────────────────────────

  - ca-certificates    2023.5.7  hbcca054_0    conda-forge                 
  + ca-certificates  2023.05.30  h06a4308_0    pkgs/main/linux-64     123kB
  - openssl              1.1.1t  h0b41bf4_0    conda-forge                 
  + openssl              1.1.1v  h7f8727e_0    pkgs/main/linux-64       4MB

  Downgrade:
─────────────────────────────────────────────────────────────────────────────

  - beautifulsoup4       4.11.1  pyha770c72_0  conda-forge                 
  + beautifulsoup4       4.10.0  pyh06a4308_0  pkgs/main/noarch        87kB

  Summary:

  Install: 1 packages
  Upgrade: 2 packages
  Downgrade: 1 packages

  Total download: 4MB

─────────────────────────────────────────────────────────────────────────────


[+] 0.0s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   0.0 B                            0.0s
Extracting       ━━━━━━━━━━━━━━━━━━━━━━━       0                            0.0s[+] 0.1s
Downloading  (4) ━━━━━━━━━━━━━━━━━━━━━━━   0.0 B beautifulsoup4             0.0s
Extracting       ━━━━━━━━━━━━━━━━━━━━━━━       0                            0.0sbeautifulsoup4                                      86.6kB @ 591.3kB/s  0.2s
ca-certificates                                    122.6kB @ 832.7kB/s  0.2s
bs4                                                 10.2kB @  67.9kB/s  0.2s
openssl                                              3.9MB @  24.0MB/s  0.2s
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Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━╸━━━━━━━━━━━━━━━━       0 beautifulsoup4             0.0s[+] 0.3s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━╸━━━━━━━━━━━━━━━       0 beautifulsoup4             0.1s[+] 0.4s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━╸━━━━━━━━━━━━━━       0 beautifulsoup4             0.2s[+] 0.5s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━╸━━━━━━━━━━━━━       0 beautifulsoup4             0.3s[+] 0.6s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━╸━━━━━━━━━━━━       0 bs4                        0.4s[+] 0.7s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━━╸━━━━━━━━━━━       0 bs4                        0.5s[+] 0.8s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━━━━╸━━━━━━━━━       0 bs4                        0.6s[+] 0.9s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━╸━━━━━━━━━━━━━━       0 bs4                        0.7s[+] 1.0s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━╸━━━━━━━━━━━━━       0 ca-certificates            0.8s[+] 1.1s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━╸━━━━━━━━━━━━       0 ca-certificates            0.9s[+] 1.2s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━━╸━━━━━━━━━━━       0 ca-certificates            1.0s[+] 1.3s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━━━╸━━━━━━━━━━       0 ca-certificates            1.1s[+] 1.4s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━━━━╸━━━━━━━━━       0 openssl                    1.2s[+] 1.5s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━━━━━╸━━━━━━━━       0 openssl                    1.3s[+] 1.6s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ━━━━━━━━━━━━━━━╸━━━━━━━       0 openssl                    1.4s[+] 1.7s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (4) ╸━━━━━━━━━━━━━━━╸━━━━━━       0 openssl                    1.5s[+] 1.8s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (3) ━━━━╸━━━━━━━━━━━━━━━━━━       1 beautifulsoup4             1.6s[+] 1.9s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (2) ━━━━━━━━━━╸━━━━━━━━━━━━       2 beautifulsoup4             1.7s[+] 2.0s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (1) ━━━━━━━━━━━━━━━━╸━━━━━━       3 beautifulsoup4             1.8s[+] 2.1s
Downloading      ━━━━━━━━━━━━━━━━━━━━━━━   4.1MB                            0.1s
Extracting   (1) ━━━━━━━━━━━━━━━━╸━━━━━━       3 beautifulsoup4             1.9s
Downloading and Extracting Packages

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Collecting nbformat==4.2.0
  Downloading nbformat-4.2.0-py2.py3-none-any.whl (153 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 153.3/153.3 kB 26.0 MB/s eta 0:00:00
Requirement already satisfied: ipython-genutils in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (0.2.0)
Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (4.17.3)
Requirement already satisfied: jupyter-core in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (4.12.0)
Requirement already satisfied: traitlets>=4.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from nbformat==4.2.0) (5.9.0)
Requirement already satisfied: attrs>=17.4.0 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (23.1.0)
Requirement already satisfied: importlib-metadata in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat==4.2.0) (4.11.4)
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In [2]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots

Define Graphing Function¶

In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.

In [3]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()

Question 1: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.

In [4]:
tesla = yf.Ticker("TSLA")

Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.

In [5]:
tesla_data = tesla.history(period='max')

Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.

In [6]:
tesla_data.reset_index(inplace=True)
tesla_data.head(5)
Out[6]:
Date Open High Low Close Volume Dividends Stock Splits
0 2010-06-29 1.266667 1.666667 1.169333 1.592667 281494500 0 0.0
1 2010-06-30 1.719333 2.028000 1.553333 1.588667 257806500 0 0.0
2 2010-07-01 1.666667 1.728000 1.351333 1.464000 123282000 0 0.0
3 2010-07-02 1.533333 1.540000 1.247333 1.280000 77097000 0 0.0
4 2010-07-06 1.333333 1.333333 1.055333 1.074000 103003500 0 0.0

Question 2: Use Webscraping to Extract Tesla Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.

In [7]:
import requests

# URL of the webpage to download
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm"

# Send a GET request to the URL
response = requests.get(url)

# Check if the request was successful (status code 200)
if response.status_code == 200:
    # Save the HTML content as a variable named html_data
    html_data = response.text
    print("Webpage downloaded successfully.")
else:
    print("Failed to download the webpage. Status code:", response.status_code)
Webpage downloaded successfully.

Parse the html data using beautiful_soup.

In [8]:
from bs4 import BeautifulSoup

# Assuming you already have 'html_data' containing the HTML content

# Create a BeautifulSoup object
soup = BeautifulSoup(html_data, 'html.parser')

# Now you can work with the parsed HTML, for example, let's print the title of the webpage:
title = soup.title
print("Title of the webpage:", title.text)

# You can navigate the HTML structure, extract elements, and perform various operations on it.
# For example, to find all <a> (anchor) tags:
all_links = soup.find_all('a')
for link in all_links:
    print("Link Text:", link.text)
    print("Link URL:", link.get('href'))
Title of the webpage: Tesla Revenue 2010-2022 | TSLA | MacroTrends
Link Text: 
Link URL: https://www.macrotrends.net
Link Text: Stock Screener
Link URL: /stocks/stock-screener
Link Text: Stock Research
Link URL: /stocks/research
Link Text: Market Indexes
Link URL: /charts/stock-indexes
Link Text: Precious Metals
Link URL: /charts/precious-metals
Link Text: Energy
Link URL: /charts/energy
Link Text: Commodities
Link URL: /charts/commodities
Link Text: Exchange Rates
Link URL: /charts/exchange-rates
Link Text: Interest Rates
Link URL: /charts/interest-rates
Link Text: Economy
Link URL: /charts/economy
Link Text: Global Metrics
Link URL: /countries/topic-overview
Link Text: Prices
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/stock-price-history
Link Text: Financials
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/financial-statements
Link Text: Revenue & Profit
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue
Link Text: Assets & Liabilities
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/total-assets
Link Text: Margins
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/profit-margins
Link Text: Price Ratios
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/pe-ratio
Link Text: Other Ratios
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/current-ratio
Link Text: Other Metrics
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/dividend-yield-history
Link Text: Revenue
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue
Link Text: Gross Profit
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/gross-profit
Link Text: Operating Income
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/operating-income
Link Text: EBITDA
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/ebitda
Link Text: Net Income
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/net-income
Link Text: EPS
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/eps-earnings-per-share-diluted
Link Text: Shares Outstanding
Link URL: https://www.macrotrends.net/stocks/charts/TSLA/tesla/shares-outstanding
Link Text: Auto/Tires/Trucks
Link URL: https://www.macrotrends.net/stocks/sector/5/auto-tires-trucks
Link Text: Auto Manufacturers - Domestic
Link URL: https://www.macrotrends.net/stocks/industry/7/
Link Text: General Motors (GM)
Link URL: /stocks/charts/GM/general-motors/revenue
Link Text: Ford Motor (F)
Link URL: /stocks/charts/F/ford-motor/revenue
Link Text: Harley-Davidson (HOG)
Link URL: /stocks/charts/HOG/harley-davidson/revenue
Link Text: Polaris (PII)
Link URL: /stocks/charts/PII/polaris/revenue
Link Text: IAA (IAA)
Link URL: /stocks/charts/IAA/iaa/revenue
Link Text: Fisker (FSR)
Link URL: /stocks/charts/FSR/fisker/revenue
Link Text: Lion Electric (LEV)
Link URL: /stocks/charts/LEV/lion-electric/revenue
Link Text: Volta (VLTA)
Link URL: /stocks/charts/VLTA/volta/revenue
Link Text: Bird Global (BRDS)
Link URL: /stocks/charts/BRDS/bird-global/revenue
Link Text: Lightning EMotors (ZEV)
Link URL: /stocks/charts/ZEV/lightning-emotors/revenue
Link Text: Terms of Service
Link URL: /terms
Link Text: Privacy Policy
Link URL: /privacy
Link Text: Contact Us
Link URL: mailto:%69n%66o@%6Dac%72otrends%2En%65t
Link Text: Do Not Sell My Personal Information
Link URL: /ccpa
Link Text: Zacks Investment Research, Inc.
Link URL: https://www.zacksdata.com
Link Text: Tesla Revenue 2010-2022 | TSLA
Link URL: None
Link Text: Macrotrends
Link URL: None
Link Text: Source
Link URL: None
Link Text: Tesla Revenue 2010-2022 | TSLA
Link URL: None
Link Text: Macrotrends
Link URL: None
Link Text: Source
Link URL: None

Using BeautifulSoup or the read_html function extract the table with Tesla Revenue and store it into a dataframe named tesla_revenue. The dataframe should have columns Date and Revenue.

Click here if you need help locating the table

Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab

soup.find_all("tbody")[1]

If you want to use the read_html function the table is located at index 1


In [10]:
    # Locate the table with Tesla Revenue using the specific code
    table = soup.find_all("tbody")[1]

    # Initialize empty lists to store data
    dates = []
    revenues = []

    # Loop through the rows of the table
    for row in table.find_all("tr"):
        # Extract the columns (cells) from each row
        cols = row.find_all("td")
        if len(cols) == 2:
            date = cols[0].text.strip()
            revenue = cols[1].text.strip()
            
            # Append data to lists
            dates.append(date)
            revenues.append(revenue)

    # Create a DataFrame from the lists
    tesla_revenue = pd.DataFrame({'Date': dates, 'Revenue': revenues})

Execute the following line to remove the comma and dollar sign from the Revenue column.

In [11]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")
/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version.
  """Entry point for launching an IPython kernel.

Execute the following lines to remove an null or empty strings in the Revenue column.

In [12]:
tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.

In [13]:
tesla_revenue.tail(5)
Out[13]:
Date Revenue
48 2010-09-30 31
49 2010-06-30 28
50 2010-03-31 21
52 2009-09-30 46
53 2009-06-30 27

Question 3: Use yfinance to Extract Stock Data¶

Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.

In [14]:
gamestop = yf.Ticker('GME')

Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.

In [15]:
gme_data = gamestop.history(period='max')

Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.

In [16]:
gme_data.reset_index(inplace=True)
gme_data.head(5)
Out[16]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 1.620129 1.693350 1.603296 1.691667 76216000 0.0 0.0
1 2002-02-14 1.712707 1.716073 1.670625 1.683250 11021600 0.0 0.0
2 2002-02-15 1.683251 1.687459 1.658002 1.674834 8389600 0.0 0.0
3 2002-02-19 1.666418 1.666418 1.578047 1.607504 7410400 0.0 0.0
4 2002-02-20 1.615920 1.662210 1.603296 1.662210 6892800 0.0 0.0

Question 4: Use Webscraping to Extract GME Revenue Data¶

Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data.

In [17]:
import requests

# URL of the webpage to download
url = "https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html"

# Send a GET request to the URL
response = requests.get(url)

# Check if the request was successful (status code 200)
if response.status_code == 200:
    # Save the HTML content as a variable named html_data
    html_data = response.text
    print("Webpage downloaded successfully.")
else:
    print("Failed to download the webpage. Status code:", response.status_code)
Webpage downloaded successfully.

Parse the html data using beautiful_soup.

In [18]:
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.text, 'html.parser')

    # Now you can work with the parsed HTML content as needed
    
    # For example, you can print the title of the webpage:
    title = soup.title
    print("Title of the webpage:", title.text)
    
    # Or you can find specific elements in the HTML using their tags, attributes, or other criteria:
    # Example: Find all <a> (anchor) tags
    all_links = soup.find_all('a')
    for link in all_links:
        print("Link Text:", link.text)
        print("Link URL:", link.get('href'))
Title of the webpage: GameStop Revenue 2006-2020 | GME | MacroTrends
Link Text: 
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/
Link Text: Stock Screener
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/stock-screener
Link Text: Stock Research
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/research
Link Text: Market Indexes
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/charts/stock-indexes
Link Text: Precious Metals
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/charts/precious-metals
Link Text: Energy
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/charts/energy
Link Text: Commodities
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/charts/commodities
Link Text: Exchange Rates
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/charts/exchange-rates
Link Text: Interest Rates
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/charts/interest-rates
Link Text: Futures
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/futures
Link Text: Economy
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/charts/economy
Link Text: Global Metrics
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/countries/topic-overview
Link Text: Prices
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/stock-price-history
Link Text: Financials
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/financial-statements
Link Text: Revenue & Profit
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue
Link Text: Assets & Liabilities
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/total-assets
Link Text: Margins
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/profit-margins
Link Text: Price Ratios
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/pe-ratio
Link Text: Other Ratios
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/current-ratio
Link Text: Other Metrics
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/dividend-yield-history
Link Text: Revenue
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/revenue
Link Text: Gross Profit
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/gross-profit
Link Text: Operating Income
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/operating-income
Link Text: EBITDA
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/ebitda
Link Text: Net Income
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/net-income
Link Text: EPS
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/eps-earnings-per-share-diluted
Link Text: Shares Outstanding
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GME/gamestop/shares-outstanding
Link Text: Retail/Wholesale
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/sector/3/retail-wholesale
Link Text: Retail - Consumer Electronics
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/industry/156/
Link Text: Best Buy (BBY)
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/BBY/best-buy/revenue
Link Text: Aaron's,  (AAN)
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/AAN/aarons,-/revenue
Link Text: GOME Retail Holdings (GMELY)
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/GMELY/gome-retail-holdings/revenue
Link Text: Systemax (SYX)
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/SYX/systemax/revenue
Link Text: Conn's (CONN)
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/CONN/conns/revenue
Link Text: Taitron Components (TAIT)
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/stocks/charts/TAIT/taitron-components/revenue
Link Text: Terms of Service
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/terms
Link Text: Privacy Policy
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/privacy
Link Text: Contact Us
Link URL: https://web.archive.org/web/20200814131437/mailto:info@macrotrends.net
Link Text: Do Not Sell My Personal Information
Link URL: https://web.archive.org/web/20200814131437/https://www.macrotrends.net/ccpa
Link Text: Zacks Investment Research, Inc.
Link URL: https://web.archive.org/web/20200814131437/https://www.zacksdata.com/
Link Text: GameStop Revenue 2006-2020 | GME
Link URL: None
Link Text: Macrotrends
Link URL: None
Link Text: Source
Link URL: None
Link Text: GameStop Revenue 2006-2020 | GME
Link URL: None
Link Text: Macrotrends
Link URL: None
Link Text: Source
Link URL: None

gme_revenueUsing BeautifulSoup or the read_html function extract the table with GameStop Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.

Click here if you need help locating the table

Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab

soup.find_all("tbody")[1]

If you want to use the read_html function the table is located at index 1


In [25]:
    # Locate the table with GameStop Revenue using the specific code
    table = soup.find_all("tbody")[1]

    # Initialize empty lists to store data
    dates = []
    revenues = []

    # Loop through the rows of the table
    for row in table.find_all("tr"):
        # Extract the columns (cells) from each row
        cols = row.find_all("td")
        if len(cols) == 2:
            date = cols[0].text.strip()
            revenue = cols[1].text.strip()
            
            # Append data to lists
            dates.append(date)
            revenues.append(revenue)

    # Create a DataFrame from the lists
    gme_revenue = pd.DataFrame({'Date': dates, 'Revenue': revenues})
In [26]:
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"")
/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning:

The default value of regex will change from True to False in a future version.

In [27]:
gme_revenue.dropna(inplace=True)

gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]

Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.

In [28]:
gme_revenue.tail(5)
Out[28]:
Date Revenue
57 2006-01-31 1667
58 2005-10-31 534
59 2005-07-31 416
60 2005-04-30 475
61 2005-01-31 709

Question 5: Plot Tesla Stock Graph¶

Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla'). Note the graph will only show data upto June 2021.

In [22]:
make_graph(tesla_data, tesla_revenue, 'Tesla')

Question 6: Plot GameStop Stock Graph¶

Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.

In [29]:
make_graph(gme_data, gme_revenue, 'GameStop')

About the Authors:

Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

Azim Hirjani

Change Log¶

Date (YYYY-MM-DD) Version Changed By Change Description
2022-02-28 1.2 Lakshmi Holla Changed the URL of GameStop
2020-11-10 1.1 Malika Singla Deleted the Optional part
2020-08-27 1.0 Malika Singla Added lab to GitLab

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